When no correlation between different samples is assumed, training samples can be used as individual inputs to the ML model. When there is dependence among different samples, they can also be passed on to the ML model as sequences of input data. The length of the sequence is a hyperparameter that has to be tuned accordingly. If it is set too small for a given problem, the network may not fully exploit the correlation between different samples. On the contrary, if the length is set too high, the different sequences may be uncorrelated. That can lead to unnecessary computations or to a sub-optimal ML model. For example, in [1]}, the authors found that using a sequence of 200 RSS samples collected at different timestamps gave better results than using a larger or smaller number of them. Similar findings were reported in [2]}, [3]}, [4]}.
